Clinical samples collected from patients often contain only a small number of cells. Despite this limitation, these samples can provide valuable information about how diseases develop, how the immune system responds, and how patients react to treatments. Extracting as much biological information as possible from these limited samples is therefore a major goal in biomedical research.

A team led by researchers at Harvard Medical School have developed a practical workflow that allows scientists to generate detailed multiomic data from small clinical samples using the Seq-Well S3 platform. Their protocol combines several techniques to measure gene activity, cell surface proteins, and other cellular features from individual cells.

Single cell technologies have transformed biology by allowing scientists to examine thousands of cells individually instead of studying large mixed populations. However, many of these methods require expensive equipment or large numbers of cells. The Seq-Well S3 platform was designed to make single cell experiments more accessible and compatible with limited clinical material.

Schematic workflows for single-cell multiomic sequencing library preparation
and data analysis processing

The dashed line boxes indicate optional steps of the Protocol. a, Sample preparation and generation of sequencing libraries. Four library types (scRNA, HTO, ADT and bulk RNA) will undergo Illumina sequencing to generate raw reads for data analysis. b, Each library type will undergo its respective preprocessing indicated by each colored box. Subsequent data integration occurs resulting in a final Seurat object composed of various data levels. 

The workflow begins with preparing a suspension of individual cells from a sample. Researchers can optionally label cells with special antibodies that are linked to short DNA sequences. These labels allow scientists to measure cell surface proteins at the same time they examine gene expression.

Next, the cells are processed using the Seq-Well S3 system to create sequencing libraries. These libraries capture RNA molecules from each cell, enabling RNA sequencing to measure which genes are active. The protocol can also incorporate SMART-seq2 bulk RNA sequencing to support genetic demultiplexing when multiple samples are processed together.

A key feature of the workflow is sample hashing, a technique that allows researchers to mix multiple samples in a single experiment. Each sample is tagged with a unique molecular barcode before sequencing. This approach reduces cost, increases throughput, and helps minimize technical variation between experiments.

After sequencing, computational analysis is used to identify individual cells, quantify gene expression, and integrate the different types of molecular data. The protocol was designed so that all steps can be performed using standard laboratory equipment and widely available reagents.

Importantly, the entire process can generate high quality multiomic data in less than a week. This speed and accessibility make the approach particularly useful for studies involving patient samples, where material is often scarce and time sensitive.

By providing a clear and scalable workflow for single cell multiomic analysis, this method helps researchers gain deeper insights into cellular behavior in health and disease, even when only small clinical samples are available.